← The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: Enterprises Will Not Adopt AI without Forward-Deployed Engineers | Who Wins the Data Labelling Race: How Does it Shake Out? | How Synthetic Data Threatens the Future of Human-Generated Data with Matt Fitzpatrick, CEO of Invisible Technologies
01:23:48
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- [00:00]
Harry Stebbings
This is 20 VC with me, Harry Stebbings, and this is the last episode of 2025. Now if you're wondering why I sound like Mick Jagger, no, it is not because I have been partying like a maniac and lost my voice over the Christmas break. It's because I have been walking four marathons in four days with my mother to raise money for multiple cirrhosis sufferers. We've raised $50,000 in the last three days. I would love your support if you want to donate to Ms. Sufferers, but that is why I sound like Mick Jagger. But to the data is everything in the world of model performance. Turing, McCor and today's guest Invisible are one of a few who have reached several hundred million dollars in revenue. And as I said, I'm thrilled to be joined today by Matt Fitzpatrick, CEO of Invisible Technologies. Now, since joining as CEO in January 2025, he's achieved some incredible milestones. Most significantly, he's raised over $100 million for the company. And as I said, he's hit the rarefied air of over $200 million in annual recur. This was an incredible show recorded in person in London and I cannot wait to hear your feedback. But before we dive into the show today, are you drowning in AI tools? Chat GPT for writing, notion for docs, Gmail for email, Slack for comms, and you're constantly copy pasting between them all, losing context and losing time. This is the AI productivity tax and it's killing your output. At 20 VC we're all about speed of execution and Superhuman is the AI productivity suite that gives you superpowers everywhere you work. With the intelligence of Grammarly, mail and coda built in, you can get things done faster and collaborate seamlessly. Finally, AI that works where you work, however you work. Superhuman gets you from day one with zero learning curve and it's personalized to sound like you at your best, not like everyone else using generic AI. Get AI that works where you work. Unlock your superhuman potential. Learn more@superhuman.com podcast that's superhuman. And speaking of tools that give you an edge, that's exactly what AlphaSense does for decision making. As an investor, I'm always on the lookout for tools that really transform how I work. Tools that don't just save time, but fundamentally change how I uncover insights. That's exactly what AlphaSense does. With the acquisition of Tegus, AlphaSense is now the ultimate research platform built for professionals who need insights they can trust fast. I've used Teagus before for company deep dives right here on the podcast. It's been an incredible resource for expert insights, but now with AlphaSense leading the way, it combines those insights with premium content, top broker research and cutting edge generative AI.
- [02:48]
Matt Fitzpatrick
The result?
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Harry Stebbings
A platform that works like a supercharged junior analyst delivering trusted insights and analysis on demand. AlphaSense has completely reimagined fundamental research, helping you uncover opportunities from perspectives you didn't even know how they existed. It's faster, it's smarter, and it's built to give you the edge in every decision you make to any VC listeners. Don't miss your chance to try AlphaSense for free. Visit AlphaSense.com 20 to unlock your trial. That's AlphaSense.com 20 and if AlphaSense helps you make smarter decisions, Daily Body Coach helps you build smarter habits. You know how so many founders and execs say they'll finally take care of.
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You have now arrived at your destination. Matt, I am so excited for this dude.
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I think Invisible is one of the most incredible but also I'm sorry to.
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Say this, I under discussed businesses when I look the incredible achievements that you've had over the last few years. So thank you so much for joining me.
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Matt Fitzpatrick
Thank you for having me. I really enjoy the show.
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Harry Stebbings
Can you just talk to me about how does a 10 year McKinsey stalwart warrior become CEO of one of the fastest growing data companies in tech? How does that transition happen?
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Matt Fitzpatrick
I would say my McKinsey journey was non traditional. I spent 12 years there. I was a senior Partner and I led a group called Quantum Black Labs which is the firm's global tech development group. So about 10 years ago McKinsey actually started hiring engineers and I was a big part of this in a pretty big quantum. And when I started we had about 100 engineers total in firm. By the time I left we had 7,000. I oversaw about a fifth of that group and all the application development, all of the data warehouse infrastructure and all of the geni bills globally. And so that journey was really interesting and over the course of it spent a variety of my time competing with other large enterprise AI businesses. And I got to know the founder Francis really well. About three years or four years ago now we actually met. Totally not work related kind of social context where we were discussing. It was basically a forum called Dialogue. I don't know if you heard it but you basically talk about different ideas we bonded over.
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Harry Stebbings
I keep getting invited to this. It's in Hawaii though.
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Matt Fitzpatrick
It's in many different locations. I really enjoy it because you actually don't talk about work at all. You're not allowed to talk about your job. You spend time talking about history, politics, technology.
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Harry Stebbings
What does everyone from San Francisco do?
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Matt Fitzpatrick
They don't talk about it for two days, which is exactly, exactly. But I actually think it's one of the few events I've been to where people are not talking their own book, they're not trying to convince you of anything and you just really. Actually I've made a bunch of really good adult friendships out of that. And so Francis and I got to know each other from that four years ago. And there had been another CEO kind of in the two years before I joined who was actually based in Australia, interestingly. And so when the business got to a certain scale, it was just time to have a US based CEO that could help take the business to the next level. And you know, it was actually Francis approached me and kind of pretty directly said, do you want to be our next CEO? And that was kind of, that was kind of what happened.
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Harry Stebbings
Was it a no brainer?
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Matt Fitzpatrick
Look, I think when you walk away from a really stable job that you really enjoy, that's always difficult. The sliver of McKinsey that I was doing I found to be one of the most intellectual day to day jobs ever. I was working with all the Fortune 1000 on every different AI topic daily and particularly in the early machine learning days kind of 10 years ago. I think we built some really interesting stuff. But yeah, I think it was kind of a no brainer in some ways Because I think when you think about it, I think this is the most interesting time to run a company on a topic that has probably existed in our lifetime, maybe the 2000s. But to run a company in AI right now is fascinating. The rate at which you can build, the people at which you can recruit the interest of customers in this topic. And so I felt like I'd spent 10 years learning one topic and now I had a chance to run a business and build it the way I wanted to build it on that topic. And that's just something you can't pass up. And even though I walked away from a fair amount, but I think that I'm much more excited about building something for the next two decades out of this.
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Harry Stebbings
When we think about decision making frameworks, I always have one, which is find someone who you respect and admire. So for me it's Pat Grady, who's the head of Sequoia. I've known him for 10 years. He's a great father, investor and husband. Three things that I care about. And whenever I have a tough decision, I'm like, what would Pat do? And most of the time I get to the answer by asking that question in that framework. If I were to ask you, what do you ask yourself? How do you find direction when struggling with a decision?
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Matt Fitzpatrick
I'm not a particularly materialistic person. I think when I was coming out of college, for example, everyone was focused on going into large finance jobs, which at that time were pre financial crisis, obviously where a lot of that was. And I think a lot of what I think about is doing work day to day that I really enjoy with people I really enjoy and then building something. And I do think I really enjoyed the decade I spent building at McKinsey. I think that was an incredibly interesting experience to stand up something of that scale within an existing institution. And then I do think about, I read a ton about everything from military history to current entrepreneurs to enterprise executives I really admire. And then I have a group of, kind of a small group of people whose opinions I ask pretty regularly. And probably the most telling piece of advice, my girlfriend and my main mentor. Both of them when I asked within two minutes were like, absolutely, do this. My main mentor is a guy named Sumesh Khanna who had been a senior partner McKinsey for a long time on the board of a whole variety of different companies today. And I remember we got lunch, I walked into the opportunity, I said, listen, it's big risk. And he goes, the only risk is if you don't take this and the amount of regret you'll have not give it a go.
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Harry Stebbings
I totally agree with that one. I was once given advice that whatever you think you should do, hold that close and then let your girlfriend tell you what you should do.
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And that's why you still have a relationship.
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It's a great piece of advice. That was from someone who's been married for 40 years and so it's worked well for him.
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We were chatting before and I said.
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Listen, where do we have to go? I always think that the best conversations are led by passion. The first one that you said was, there's a gap or a chasm between model performance and adoption. When we break that down, can you explain to me what you meant by that and how we see that in action?
- [09:40]
Matt Fitzpatrick
Yeah, and let me set the context and I'll go into more detail later. But Invisible is an interesting business in that we both train all the large language models with reinforced learning feedback. And we are at the core a modular software platform where we in enterprise context, we deploy all different enterprise use cases. And I think the cognitive dissonance that has occurred in the last couple years is model performance has increased exponentially. I don't think anyone doubt that. If you look at all the public benchmarks, models have increased 40 to 60% in performance over the last two years. And consumer adoption has been also exponential. So KPMG just released that 60% of consumers use Gen AI weekly now, but the enterprise is not, you know, I think in the enterprise. MIT just released this report that 5% of Genai deployments are working in any form. I think you've seen Gartner saying 40% of enterprise projects will likely be canceled by 2027. And I think the reason for that is deployment of the Enterprise is a lot more than just models themselves. It's the data infrastructure to support those models. It's the redesign of workflows. It's the process figuring out which operational leader takes accountability for that. And then most importantly, it's trust, it's observability. All the things that I spent a decade building, things like credit models in banking. And in those cases, you need to go through model risk management, testing, training, validation. And so I think that whole process is in the first inning in the Enterprise. I think it's going to take a decade, not two years. And I do think that is the core mission that we think a lot about is I actually think the evolution of deployment of AI will be what the model builders have done for the last couple years. You'll see banks and Healthcare firms start to do the same sort of testing and validation over this period and then the rest of the enterprise will be over the next five, six years after that. And that's the journey that we're focused on.
- [11:19]
Harry Stebbings
I was speaking at one of the largest banks in the world. Absolute joke that they get a university dropout like me to speak at their largest. I find it very fun. But I left and I messaged the team and I just said, oh my God, they're toast. And they're toast because I said about the amazing tool they should implement internally. And the CTO laughed at me. He was like, dude, there's no way that we can ever adopt your off the shelf search engine optimization for LLM tool because of data, because of security, because of permissions. And I was like, wow, everything that you just said there, I listened to.
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Yeah, but that was once you got in the door.
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Are enterprises even open for business? You see Goldman Sachs developing a huge amount of their own tools. Are they open for AI business?
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Matt Fitzpatrick
Yeah, it's a great question. I think it depends a bit on the sector. I think there are sectors like banking that are very focused on building this internally. I think that is a reality.
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Harry Stebbings
Do you think that will work, the internal build for them?
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Matt Fitzpatrick
So it's interesting if you look at the MIT report, which is the one I mentioned, that says 5% of models are making a production right now, they actually cite a stat that externally driven builds are 2x as effective as internal team builds. I actually think there's an interesting kind of 10 year pattern on this, which is 10 years ago everyone bought software, right? Like that was your tech team did not try and build anything and you started to buy and you bought, you know, often you bought way too many apps, but you bought 15 different apps and that was what the technology team did. And then I think with the advent of cloud, you started to have a world where the technology function started to start to think about building things. Maybe they started to have more, some custom applications that wrapped around that. I think Genai has 5x that where now an internal team has given this enormous budget and said kind of go have at it. And I think that's complicated because I think when you hire somebody to build any vendor of any kind, you're pretty disciplined about what are you delivering, on what timeline, what's the ROI of it, what are the milestones, how does that. And I don't think that that discipline exists in the same way in internal builds. I also think that the talent levels, often the internal teams have are challenging.
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Harry Stebbings
And so when you say the internal team builds are challenging, there are some things that you can't say, but I can. The perception from external or from general kind of tech crowds is the internal teams for, I don't know, you name your boring large enterprise. It's just really low quality. You're not getting the top tier AI engineers, you're not getting top tier devs. Is that true?
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Matt Fitzpatrick
Look, I think the amount of talent that knows how to do this well is not large. And so that finite group mostly works in AI startups of various forms. Right. And large tech companies. And so I do think there's real risk to the process of figuring this out from first principles and enterprises. Right. And I think that's part of the cycle that we're going through right now is a lot of internal groups have gone through the process of saying we must do this all internally. But the reality is, if you think about that, this is an open architecture ecosystem and you're going to adopt things like MCP or you know, all the new voice agent that comes out, you actually want a modular open architecture where you can use all the best tech available and figure out how to link it together. And I think the desire to shape that all internally has been challenged. Like, I'll give you, I'll give you one of the more interesting examples I can discuss. I was talking to an E commerce retailer that had built an agent to handle their returns process and they spent 25 million bucks building this agent. And at the end of it I said, well, how did you define this was after I'd met them, after they built it. And I said at the end of it, how did you define if this agent worked or not? And they're like, well, we built our own eval tool, it's not a joke. And we basically analyzed a mix of speed of call, resolution and sentiment. The problem with that is what if the agent hallucinates and says, here's $2 million that actually gets resolved quickly and the person's happy. And so they had built this entire system from first principles. And what ended up happening was a couple months later they shut it down and moved back to a deterministic flow. And that's not surprising to me at all. And so I do think that's a little bit of the adoption curve we're in is over the next two years you're going to see the CFO function, put different guardrails on how this stuff is built and say, what is the roi? What are you investing in? What's the metric, what's the return? And that will change the adoption curve. But right now there have been a lot of science projects. I think that is a realistic.
- [15:20]
Harry Stebbings
Okay, we have hundreds of thousands of listeners and many of them are CEOs. If you are a CEO thinking about your CFO being equipped to buy and to manage in this new environment, what should they be thinking about? And do we have the right CFO talent pool to manage this new environment?
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Matt Fitzpatrick
Yeah. So I think one misconception is that that leader has to be highly technical to make that decision. And I would actually argue they don't at all. They just need the same muscle memory they've looked at in the which would be, what do you need to get a Genai initiative working? You need good data that you can work off of for that specific initiative, clear milestones and outputs, clear line ownership of the initiative, and then probably most importantly, you want to actually anchor it in milestones and outcomes where you pay as it works. So I think the other interesting context for a lot of this is what I would call the Accenture paradigm of the last 20 years. Right. Which is a lot of times the way that if you think about the wrapper that's been around software for the last 20 years, you know, our founder Francis Radazza has the founding principle of Invisible was if there's an app for everything, how come nothing works? And it's an interesting concept, right, because what ended up happening is you bought 50 apps, you had Accenture come in, and you paid them $200 million over two years to try and layer them all together. And often you ended up a couple years in with no working data, no linkages between them. And that kind of layers of sediment has been how the tech paradigm work in the enterprise for the last five years. And I think what's different now is if you're thinking about a specific gen AI initiative like a contact center, let's say you don't need to operate that way. You can think about what are the operational metrics you want in your contact center. You want to think about call resolution, call performance, cost per call, routing logic. You know, you can then look at both internal and a set of vendors who will deliver those metrics and make an evaluation. And if the vendor doesn't work, you fire them. And I think there's a very clear way to get ROI in this, which is figure out the list of three to four things that move the needle for your business. Focus on those three to four. Don't spend money on a thousand science projects. Take your best four operational leaders and put them on those four things. Don't locate it in the tech function. That's the main advice I give people is your Genai initiative should be led by the business and figure out that could be your head of call center, that could be your head of operations. But each of those people with clear operational KPIs will get their stuff working. And there are a bunch of companies that have. But it's just a very different approach than I'm building Genai as an example.
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Harry Stebbings
It's really interesting. You said don't invest in a bunch of science projects, do three to four initiatives. Okay, let's do three to four initiatives again. Let's put on that CEO hat, that contact center, it's just a big one that is homogenous across everything. Matt, there's so many players in the contact center space. I'm a CEO, I'm not a Silicon Valley guy. How am I meant to understand whether we go for Sierra or Decagon or Zendesk of old or Intercom or any of the other players that we've seen in the space? How do you Advise the biggest CEOs on buying in a wave of new innovation?
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Matt Fitzpatrick
I think this is the other big challenge of Genai adoption is you're an average CTO COO. You've got 250 vendors a week pitching you. All of them sound pretty similar. In fact, I was with a customer yesterday who literally started the meeting by saying how are you different than the other 250 people that have pitched me this week? So this is the dynamic of we have an oversaturation of companies that all sound relatively similar relative to agents. To make your question even more pointed, a lot of them don't work. I think you've got a fair number of the enterprise agent companies that Salesforce AI Research released this report that if you test a lot of the out of the box agents on single turn and multi turn workflows, they're about 58% accurate on single turn and 33% accurate on multi turn workflows, which means they don't really work. And so you've got this challenge of 250 companies a week pitching you. You don't really know how to select it and you're worried you're going to pick someone that's effectively Charlotte and it won't work. And the more you have a market where there's a lot of excitement, the more you do have that risk. Right? So I think the simplest advice I give, and by the Way, this is how we sell, quote, unquote, is start with proof of concepts, start with we call solution. Sprints don't pay a dollar until you prove the tech works. So like, we don't actually sell anything. We meet a customer, we say we will, we will do it for free for eight weeks and prove to you the tech works. And that's a very simple way. If your tech works, you'll show it.
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Harry Stebbings
It'S an expensive way to do business.
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Matt Fitzpatrick
It is and it's not. But so let me give an example, like how one of our deployments works, because I think it fair enough if the answer is that, you know, it takes you two years to build anything. But like, I'll give you an example. So our AI software platform is effectively five modular, modular components. So Neuron, which is our data platform, brings together structured, unstructured data. Axon, which is our AI agent builder, Atomic, which is effectively a process builder. We can build any custom software workflow and then we have a meridial expert marketplace, which is, we have 1.3 million experts a year on any topic you can imagine that we bring into those workflows. And then synapse, which is our valuation platform, and all of it. Now we can take those five things and configure them to almost any different enterprise context. So just an example, we serve food and beverage, public sector asset management, agriculture, sports, oil and gas, a whole host of different sectors using that same modular architecture. I think we end up scaling pretty materially once we show what the tech works. We're working with a company called LifeSpanMD, which is a concierge medicine business across the US and internationally. And what we're doing for them is we're building them an entire tech backbone where they have an enormous amount of fragmented data across ehr, CRM, ERP systems, notes, everything else. All of their data sits in a pretty fragmented format. And so we're using Neuron to bring all that data together. We do that very, very fast. So if Accenture would take two years, we can usually do it in two to three months. We're then on the back of that building a lot of different intelligence and reporting. So they can look at things like patient journeys over time, labs, genomics, data, how much you use, like the OURA ring or anything else like that. But they want to look at wearables, how all that content is looking. So they have a lot of detail on what any patient is doing at one time. And then, then on top of that we layer things like we have the ability to Interrogate the data and ask lots of different questions like let me look at who's used peptides to male between 36 and 50 and what have been the results. So we're using Axon to build all that and then we build and to fine tune a model to do that. And then we actually do also on top of that build lots of specific custom agents for things like scheduling. So what you get at the end of that is a transformed tech enabled business with all of those different components. Now that does take us a little while to stand up, but once that is there, it's effectively hyper personalized software. And that is my view on where this whole industry goes is you move from SaaS out of the box SaaS to much more hyper personalization using the specific data of an individual customer. And that is what we do.
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Harry Stebbings
Do you think you can work with enterprise today with gen AI and with AI implementation without an intense fully deployed engineer mechanism?
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Matt Fitzpatrick
I don't think you can. So we've doubled down. A huge part of what we do is for deployed engineers. So we now have eight offices, eight cities, 450 people were fully focused on forward deployed engineering. And I can tell you from a decade of my prior life, you just cannot do this without the box SaaS. It does not work.
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Harry Stebbings
What do the economics of FDEs look like? Obviously Palantir has made it the most sexy thing ever. I love the way tech crowds work where it's like we all just kind of get super excited by like an acronym and it's like this is the coolest thing, but what do the economics look like?
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Matt Fitzpatrick
Well, one thing I'll say is forward deployed engineering has come to mean a lot of different things. So a lot of forward deployed engineering, I think across the broader market is more like kind of solutions engineering or the people that kind of answer your questions and show up at your office. I think forward deployed engineering done well is executing a very specific workflow build. So you're effectively configuring a set of core platforms to build something hyper specific for that customer. And usually one of the questions is it depends on how good your platform is. Because for example, you could argue Accenture is forward deployed engineering. Right? But that build may take three years and in our case I think we've built modularity and built a lot of the new software, workflow development workflows into what we do. And so usually our four deployed engineering motions are about three months. So we will come on board, customize everything to the hyper specific way a customer wants it, and Then build something on basis works. And it does require ongoing fine tuning. So that's the other big difference that people should acknowledge, right, is that you can't fine tune a model in an enterprise context and just leave it for four years and hope it continues to work. I could give you 100 examples. But take health care, GLP1's launch, you do need to fine tune the model for the new context of the market. And so we do view it that.
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Harry Stebbings
Way, but I'm very naive, so forgive me on this. Do they pay additional for FTEs to come? Do you pay additional in terms of ongoing maintenance? Just on the economics of it.
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Matt Fitzpatrick
For many of our competitors, they do charge. We do not charge anything for fds.
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Harry Stebbings
Why not?
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Matt Fitzpatrick
I think it goes back to my general premise that the best way to differentiate in this market is to prove that your tech works. And so the way that we do this is we say you will pay when the software is up and running. And we're able to do with one to two person small FD teams a lot. And so once that's set up and running, then we do have ongoing software that is. You know, I think the paradigm that we're evolving from is over the last 20 years you had kind of the system of record layer was where a lot of the value sat. And what we're building is hyper personalized system of agility layers. Kind of what sits atop that. I think the Accenture paradigm is what people are afraid of and it's very hard to convince somebody you're going to pay tax materials until it gets working. And so I spend less on sellers and more on for deployed engineers. That's my simple math.
- [24:25]
Harry Stebbings
I always think the biggest mistake that people have is they don't put the hat on of their customer. I think the reason the show has been successful is because I put the hat on of different customers. A lot of the customers that we have is startup founders who create amazing products and everyone wants to sell into enterprise. That's where the money is. If I'm a startup founder thinking, huh, do we need FDs? How do we do FDs? How do we move into an FDE model? What would you say to them? That they should know if they're thinking about starting that model or potentially needing that model. Knowing all that, you know, I think.
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Matt Fitzpatrick
It depends a lot on the nature of the business and what you're trying to build. You know, if you're trying to build a knowledge management system of public filings for finance, for example, you don't need fds because what you're building there is a repository of information that people can access. You've seen similar things in healthcare. For example, if you're trying to change workflows, you do need FDs. I think that's the simple paradigm difference in my mind is if you're building something where the hardest part is getting adoption and workflow embedding and you need to actually change the way a company works, then yes, for deployed engineers are the only way to do it. It's interesting there aren't that many folks that have expertise doing that, so it's a hard thing to train and learn. But I do think it is the only way to get the enterprise working.
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Harry Stebbings
You've said several times, hey, don't pay until you prove that it works. And you said earlier, pay as it works. That's not the SaaS business that we've been trained on. Matt, an MSAS investor, How does the pricing model of the future look in this very new environment?
- [25:53]
Matt Fitzpatrick
Let me step me back for a second. I think an interesting thing, if you look at the economics of SaaS and enterprise five to ten years ago, and I think it's an interesting look at any large public enterprise software business and then look at how much of their revenue is actually services. And I think you could kind of argue that out of the box software has always been a lie to some degree. It's a weird thing to say, but they always had a ton of configuration and they just dressed it up to some degree. I think SaaS was even more challenging than that because often the unit economics of SaaS you're selling a much smaller cost per customer. The SaaS business that worked was actually about selling something where the out of the box setup was quick enough that you could make it work with the sales team where you didn't have to do lots of configuration because the minute you had to bring in FDs in a SaaS context, your economics broke instantly. Right? And what I'd say then on the enterprise side, the way people made it work was that's why Accenture grew so much. That's why cognizant, that's why TCS grew so much, is I'll give an example. Like if you take insurtechs as an example, right, Every one of the major insuretechs like a duck creek, like what they have is a set of core data schemas, a series of analytical logic and a front end. And the ones that did really well had momentum and push from the SIS that got them going. And so their Economics were geared by having somebody else do all your services around what you did and then you got something standing up at the end that worked. I think the challenge with Genai is that motion doesn't really work because what ends up being built at the end of the day is something that is hyper specific to that customer. Like if you actually think about the nature of it, fine tuning an LLM or creating a knowledge management system, it's not a box. It's not. It is something that uses a lot of different consistent tooling, but it has to be customized. And so the way we do that is we stand that up, we get it working and at the end of it, usually two to three months in, the payment happens when we pass user acceptance, testing and validation and it works. And here's the other thing I'll say is we use SaaS as a paradigm because that's how software has worked. But machine learning has been around the enterprise for. I was building machine learning models 10 years ago, that's always been a motion that looked like this. So what's happening now is we're starting to realize that the Genai adoption paradigm in the enterprise works the same way that ML did.
- [27:56]
Harry Stebbings
When we look at the different products that we have today, the expert platform is one, I think that gets a lot of attention. How much of the business today is the expert platform? I find companies are lumped into categories. It's easier and you have your Macaws, your Surges, your Invisibles, and you're all kind of put in this like, are you all just talent marketplaces? And no one wants to be a talent marketplace, it seems. And I'm like, how much of your revenue is the talent marketplace?
- [28:20]
And why does no one want to.
- [28:21]
Be a talent marketplace?
- [28:23]
Matt Fitzpatrick
I actually think the AI training space has many different players that do have many different business models within it. There's four to five, but actually they're all quite different. I think of us much more of an AI training platform than just a talent marketplace. Meaning we have 1.3 million experts that come through the marketplace. But a lot of the expertise we've built over the last 10 years is the ability to. Here's the simplistic question I think that AI training asks. You have to be able to source any expert in the world in 24 hours notice. You have to be able to source a PhD in astrophysics from Oxford, put them into a digital assembly line in four days later, generate perfect statistically validated data that will be compared head to head somebody else's data, and make sure that that is perfect at the end. That is an incredibly difficult thing to do. And so actually a lot of what I saw when I took over invisible was that motion was incredibly applicable to actually the next phase of the enterprise as well, which is the fine tuning motions, the training, the ability to statistically validate for an enterprise use case like claims processing, it's the same motion. Like I actually think AI training will be used next in, in banking and healthcare and then after that in many other different enterprise contexts. And so the historical business I took over in 2024 was pretty materially weighted to the AI training side of the house. But I came in with a thesis that enterprise would be a huge source of growth. And I think as you see next year evolve, I think we've confirmed 12 enterprise deals in the last 45 days. So we see pretty good momentum on that side of the business. And I think that's where we will evolve is to doing both. I think the five core platforms we have allow us to serve a whole host of different end markets. And I do think that's very different than the other AI training players you mentioned. I think we're the only player that spans that broad based view in the.
- [30:02]
Harry Stebbings
Same way on the talent marketplace side. How much of the business is that today then?
- [30:06]
Matt Fitzpatrick
I won't say an exact number, but it was a pretty material percentage of 2024.
- [30:09]
Harry Stebbings
Okay, got you. So it's a pretty material percentage. The one thing that's also striking is the concentration of revenue to a couple of core players. When you look at other providers, it's like two players that make up more than 50% of revenues and for pretty much every provider. Is that the same for you and how do you think about what that revenue makeup will be given the enterprise diversification that you're talking about?
- [30:32]
Matt Fitzpatrick
Yeah, I do think for this is a space where there are not that many players that are actually building lm. So by definition the whole space has concentration. I think. I would not disagree with that. I do think that's one of the really interesting things for us on the enterprise side is we have materially more diversification now in the number of customers, customers we serve on a whole different range of topics. I also think you're seeing more kind of early stage model builders as well that are building hyper specific topics. And so that's the other part of where we see expansion in the total customer base.
- [31:02]
Harry Stebbings
When you come to negotiations with a client, given the revenue concentration, how do you play that staring contest? Because essentially they go, we know that we are one of your core customers and we will squeeze you on price. And you go, I know I'm one of your core data providers. I will stand firm. How do you handle that negotiation? Because it is a staring contest of sorts.
- [31:25]
Matt Fitzpatrick
I think people are willing to pay for good data. That's my simple firm. If you think about the importance of these models, if you think about the cost of compute, that is actually a huge chunk of the cost base. If you think about one week of bad data burns a lot of compute. I think what we've seen, the reason it's been the same four to five players in this market for a couple of years now, is it's really hard to do well. And so people are willing to pay for good data. And so I think we have a very collaborative dynamic with all of our customers on that front. I think that when you provide a service that's helpful, people are willing to pay for it. And if you provide a service that doesn't work, people don't pay for it. And so the interesting thing I would say on that front is the discussion topics anchor around again, proven value. So we'll get a topic that'll come in like a multimodal audio model, for example, and we'll go head to head with somebody on that that week. And at the end of it, we win or we lose. And so if you win and your data is way better, people are willing to pay for that.
- [32:14]
Harry Stebbings
I had a chat last night with a board member of another of the companies in the space, and he said two things that really stood out to me. He said, I'm just drastically shocked at the lack of price sensitivity for the core customers. They're willing to pay pretty much anything. Is that the case or is that a bit of an exaggeration?
- [32:32]
Matt Fitzpatrick
I think it's an exaggeration. I think in any. If you think about classic economics, people are willing to pay a fair price for good data. And so. So I don't think we operate in a model of trying to give anything unreasonable. I think there's actually fairly standard price bounds across all the players here.
- [32:45]
Harry Stebbings
Is data commoditized?
- [32:46]
When I think about pricing power, I'm a massive fan of Hamilton Helmer's seven powers. It's amazing when you think about pricing premiums. You get that through not being a commodity, through owning supply of a rare asset. Is there commoditization of data and we're kind of in a race at the bottom on the pricing of that data, or do you own the supply of vet Workflow data for surgeons in Oklahoma.
- [33:13]
Matt Fitzpatrick
Yeah, so let me take that. I'll actually start with the market context and then I'll actually use seven Powers. It is a great book and I'll use one of his frameworks for that. Like I think the market context that is somewhat misunderstood here is the way that human data becomes more and more important over the next decade. And I think the reason for that is if you, you thought of the different types of things you could train off of. So synthetic data gets mentioned a lot but like most of the time synthetic data is useful for things like let's say base truth information like math, where there is a clear output that is right or wrong. Now let's take all of the different reasoning tasks like a multi step reasoning task, like I mean even a simple one like what movie would I select based on, you know, these five preferences and then let's take that question and add into it audio, video, multimodal language, the ability to do it in 45 language language context. So the ability to think about computational biology in Hindi vs French vs English vs English with a southern accent like that paradigm is actually incredibly hard to train on. And we're still in the first inning of a lot of those permutations of complexity is what I would say. And so for a multi stage reasoning task that requires a PhD in multi different languages and human feedback is going to be important in that for the next decade. I have a strong belief on that and that was a actually one of when I chose to take this job that was actually one of my core convictions is the enterprise is going to need that too because actually a lot of if you take legal services for example, a lot of the way you're going to need to validate that is with legal expertise. There's no corpus of information you can train from. So I would start with the idea that I think the market tailwind for the next 10 years we're actually in the first inning because there's the LMS, then there's the more sophisticated enterprises and then there's everyone else that needs to train, validate and move to fine tuning. So again contrasting there's like the pre training and LM work but then to fine tune a model to a specific context, most companies don't even know what that is in the enterprise yet. And that whole process we're in the first inning of. So I think the market demand is going to continue to grow pretty materially for a decade. The Hamilton Helmer framework is an interesting one because my favorite example is he talks a little about what he calls institutional memory. He mentions the Toyota production system as an example. Right. Where Toyota would literally say to people, this is exactly how our factories are set up and nobody could replicate it. Right. I think the interesting thing about this space and why you've had a consistent set of folks doing it for a while, is to go through the process of every week having to spin up. We have 1.3 million active agents or kind of experts that come into the pool at any given week. We have 26,000 of those that we've selected that have to start in 24 hours and produce perfect data. Think about the challenge of scaling an organization that for five years can do that at really high quality and consistently turn and evolve to the different permutations of the market. New ideas of training. It's really hard to do. And I think that was what got me most excited when I took the invisible job was the question of can you make AI work in a really complicated context? Very few companies know how to do that on the enterprise side or on the training side of that for that matter. And so I thought that was a really unique institutional memory context. It is a digital assembly line, no different than an auto factory. And I think that is a hard thing to replicate.
- [36:23]
Harry Stebbings
The other really interesting area that this board member said to me was he very much agreed with you. He said exactly the same words as you in terms of first innings of data, in terms of just how much market size will increase.
- [36:33]
Matt Fitzpatrick
He said.
- [36:34]
Harry Stebbings
The other thing that I really didn't understand when I made the investment was the specialization of data and how we are moving into the acquisition of this kind of insanely niche data supply pools where it's not like cat hedge, zebra crossing. Zebra crossing is a. What do you guys call it, a pedestrian pathway. I did not see the specialization in the unbundling. Is that something that you see too in terms of these very micro niche specialized data requirements?
- [37:03]
Matt Fitzpatrick
Absolutely. I think five years ago this space was what I would call cat dog, catdog commodity labeling. I don't think anyone, and I think there was a lot of Google sheets in that era and you've seen some comments on it like, like this sector has evolved the same way most technology sectors do. Where it started with Google sheets and cat dog labeling and it's evolved to real digital assembly lines. Huge velocity of expertise and incredibly specific expertise. So like, you know, we have to give a funny example, we have to be able to validate an architectural expert on 17th century French architecture who speaks French. I mean, that is a complex thing to do on 24 hours notice. Right. And so the ability to, to source, assess, validate. And I think one of the advantages for us is because we have five years of data on who's been good at what task. There's real institutional data memory and how you do that selection and assessment. I think that's one of the core advantages we have from that.
- [37:52]
Harry Stebbings
How important is pay? You know, I think a couple of other providers have said that, Bunny. It's about how much you pay. You pay more than the others. You'll get a good talent.
- [38:00]
Matt Fitzpatrick
So a weird analogy. I think of our business like Uber. We source talent at the price at which people will do the work that is asked of them.
- [38:07]
Harry Stebbings
Right.
- [38:08]
Matt Fitzpatrick
So the same way I do that, if you're standing, standing on a street corner, your question is, can I find a ride that will pick you up at this moment within three minutes? And that matters. That's a different price if it's raining, that's a different price if you're in, you know, Rio de Janeiro versus London. Right. The price depends on the market context and the specific place you are. I think extra pay is the same dynamic. Really. A lot of what we're doing is what I call price discovery. And so the nuance I would add to what you're saying is you can overpay a really bad expert. And that is a total waste of everyone's time. And so what I think our customers appreciate is we can tell you between $150 expert and $130 expert. The difference in expertise you get.
- [38:43]
Harry Stebbings
Do you think you have control of a finite supply of data providers? If you look at the seven powers in Hamilton Hamilton, one of them is like acquiring finite supply.
- [38:53]
Matt Fitzpatrick
So I actually don't think finite supply matters. And what I mean by that is I think the expertise needed varies so much month to month that if you tried to do a world where you bottled up whatever supply it is, it would change in three months. And we actually relish that concept. I actually think the dynamic, again, why I would use Uber and Lyft, you could use Airbnb and VRBO as the same context is I don't think experts go on five platforms. Right. I think actually what you want to be is this is a two way marketplace where you need enough demand for people to be interested and you need enough expertise that many experts. And I think the reason we get 1.3 million in bounds is because of that kind of supply demand balance. So I don't think this moves to A world and actually I would never say move to a world where there is one player coming out of this. I think there's benefits to everyone to having numerous players that do AI training. And so it's a question of being one of the players that has that balance.
- [39:41]
Harry Stebbings
You said there about kind of the switching of preference of like, oh, three months ago it was this that you want. Now it's something completely different. Switching cost is another. When you have data providers in this way, are there inherent barriers to switching? Is there any loyalty?
- [39:56]
Matt Fitzpatrick
Yeah, no. I think that if you've learned how to do a certain data task really well, there's incredible value in that. And let's take the enterprise context again, because I do think it's a good one. So I'll give you an example. We're doing a lot of fine tuning on some pretty interesting topics. One example, we worked with saic, Vantor and the US Navy on fine tuning a model for underwater drone swarms. And so the question on that, if you think about niche, very niche, that's why I use as an example to answer your question. So if you thought of in that context, you've got a bunch of underwater unmanned vehicles and they're getting in all the drone and sensor data from the interaction patterns of those vehicles. And what they want to know is an object is in the water near them, what do they do? Do they react? Do they pull back? Do they alert another drone, do they engage? What are the topics of that? So fine tuning a model to take in all that complex sensor data, fine tune it, train it it and build a decision making framework for those drones. There's a lot of logic built into that and I think that's why it's been a great partnership with SAIC and Vantor, because we built logic on how to do that and it's, you know, I think that there is real sustainability and expertise. You, you build up. And so the way I think about like our, our enterprise motion, for example, is every sector is led by somebody with deep, deep sector expertise and we do build real logic on those topics. And I think the same is true for multimodal video and audio. It's true for legal. I actually think a lot of the training work, even to the model builder side. Now one interesting view I have is people talk a lot about the public benchmarks. That tends to be one question you get a lot is like, are we reaching a point where models are not improving? I actually think it, I think about it very differently, which is the models are now all moving down hyper specific things where there's not a public benchmark for them by definition. Right? Like they're moving to more very specific tasks that are very different and not something you can publicly benchmark in the same way. And that's why we do see more and more model improvement every day. But both in model builders and enterprises on these specific tasks.
- [41:48]
Harry Stebbings
You said about kind of the benchmarks, I'm just so interested. Gemini 3 killed it. It's the best ever. And Then yesterday Opus 4.5 killed it. It's the best ever. Next week Sam's going to release one. Does it matter? Are we in a world of such transient and flux where really we should detach ourselves from these just bluntly updates to last for days?
- [42:11]
Matt Fitzpatrick
Look, I think the benchmarks are a useful framework for society to gauge progress on this topic and it's a very often discussed topic. So people want a way to answer the question about are the models improving? And I can tell you unequivocally the answer is yes. I mean I think by every measure you look at they are. And they're not only improving on the benchmarks, but even on specific tasks like research for investments, for example, you can see the models are much better at doing certain tasks. And I think what you're seeing start to happen is people, and we're doing this as well, are building very specific work based benchmarks to calibrate certain things like how well does the model do on building an LBO model for example. And you're going to see more and more benchmarks cited. Now the complexity then becomes if you move from five main benchmarks like SW bench and others to 600 benchmarks, then you kind of lose track of what's doing, who's doing well and which things. But I think my interesting view on that would be I'm not sure the benchmark progress is what determines enterprise adoption. And what I mean by that is if you take the fact that the models have improved exponentially over the last couple years and you say consumer adoption has been massive, Right. Like KBMG had this report that 60% of consumers use this on a weekly basis. The adoption curve on enterprise is not going to be a question of generalizability, it's going to be a question of hyper specific performance on a specific task.
- [43:28]
Harry Stebbings
Right.
- [43:29]
Matt Fitzpatrick
And so there isn't actually a benchmark for that. Like if I, you know, let's take a investment summary document for a private equity firm, right? There's no benchmark to say firm one. This is how you write investment committee memos does this generate something that looks, with 99% precision, like something you would, would roll out? There's no benchmark to do that. And so that's where what I see as the adoption curve is actually the fine tuning and inference layer of actually testing that getting into a place where that firm could say, like this looks good. I'm okay with this. I've. You've tested it. Like machine learning has a context. I don't know if you've heard the banks do this thing called model risk management where they actually do a whole host of validation and testing on things like redlining before they roll a model out. That's what the enterprise is going to have to do. And so it's not that the model improvement doesn't matter. I actually think the benchmarks are a good way to get some sense of model improvement, but they're almost orthogonal to enterprise uptake. I think enterprise uptake depends on trust and precision on specific tasks at 99% accuracy, not generalizability.
- [44:30]
Harry Stebbings
If those specific tasks are removed in the way that you said, like summary docs for investments, often it's done by more junior people in the earlier stages of their career when they are building and kind of scaling those skills. Do you think we will have a talent pipeline problem if we do remove a lot of those junior roles, which we are seeing in certain cases already? And I think we'll continue to see where we won't actually have the graduation pathways that lead to the leaders that we have today because we've removed those junior roles.
- [44:56]
Matt Fitzpatrick
I don't actually. So I think one of the challenges is that the adoption curve of this stuff is going to take a lot longer than people expect. So I do think I said this to you earlier. I think on enterprise, this is a five to ten year adoption journey, not one to two. And so I think you have a dynamic where people will have a lot of time to react and to think about what's useful in addition to that. And so I actually find a lot of the people coming out of college right now are some of the highest adopters of this and the most useful for these kind of tools. And so we're hiring more and more people of that profile, not less, but I think the usage curve of that group of people. Certain tasks will not be done, but there will be many more. So I'll give you an example. Accounting. If you worked at a bank example or any accounting firm in the 1980s, this is absurd to think about, but you literally calculated revenue and financial statements with a slide Rule like people literally would sit there and they would generate a financial statement manually on paper with slide rule. And that was how people did accounting. Now Excel comes around, that becomes the main tool everyone uses to do accounting. And so in theory you'd have less accountants because you went from manual generation of slide rules to Excel, which actually makes it way easier to do that. You look today we have about the exact same number of accounts, in fact the same number of junior accounts. And what's happened is people do way more sophisticated accountability scenarios with the tools they have. It's this old idea of Jevons Paradox, which is you increase consumption with advanced technology. And so the number of accountants didn't go down. You actually had way more accounting. In fact, every FPA function is probably larger now than it was 25 years ago because the work people do is more sophisticated.
- [46:28]
Harry Stebbings
I do want to go back to we said about kind of market composition and how we see the different players. Is this a market where you said like Uber and Lyft. Is this a market where there's, there's one and two players and they take the dominant market share and then there's everyone else? Is it a cloud market where it's much more evenly distributed? How do you project that out in say a 10 year horizon in both.
- [46:48]
Matt Fitzpatrick
AI training and in enterprise? I don't think the answer is one player. I think actually, interestingly in the enterprise historically it's been Palantir and not many others. So that's kind of, I think, why you've seen more people want alternative options to that. I think that's part of the reason you've seen so much excitement on enterprise AI recently. I think most of these markets end up with three, four or five players. I don't actually think it's even two. And I think the choice in consumers is markets tend to create that. And that's a good thing, right? Like I think you'll have some specialization on certain topics, you know, maybe some better at coding, some better, better at specialist tasks, some better at PhDs. But I think it'll, it'll stay with a fair amount of choice.
- [47:23]
Harry Stebbings
When you look at the landscape, who do you most respect and what do you learn from them?
- [47:27]
Matt Fitzpatrick
I would say Palantir is the company I probably respect the most in enterprise AI.
- [47:31]
Harry Stebbings
It's really interesting you see them as a competitor more than a Massage or Macaw or Turing or any of the others.
- [47:36]
Matt Fitzpatrick
I think they are both competitors in different ways to different parts. All of those players are competitors in different ways, different parts of our business. I think I call out Palantir because I think they realized 10 years before the rest of the kind of tech market that for deployed engineering, customization would be important. And I think that was a very countercultural leap at the time, you know, because, look, I mean, I spend a lot of time running for deployed engineering teams, and most of what I saw was players like Accenture, what was called tech services back then, was not a place that anyone wanted to play in. And so Palantir spent a decade before anyone realized this was important building good tech. Right. And so I have a ton of respect for that and the culture they built out of that. I think on the AI training side, I won't comment on anyone specific. I think all the players in the space are good and they all do different things.
- [48:21]
Harry Stebbings
Well, there are large revenue numbers thrown out.
- [48:24]
Matt Fitzpatrick
Yeah.
- [48:25]
Harry Stebbings
Are they revenue? Because I've done shows before with them, and I got battered bluntly when people like, oh, it's not revenue, Harry. And you can't categorize it as revenue. Is it gmv, not revenue? Are we playing fast and loose with the truth on revenue versus kind of bookings?
- [48:40]
Matt Fitzpatrick
I think it is revenue. I think that the rate you get on every project is different. The margin you make in every project is different. So I do think it is revenue. And I think that the.
- [48:49]
Harry Stebbings
Can you to help me understand. Sorry, I'm very naive. If I'm acquiring amazing talent and I get paid for that, and then I have to pay them, and then I get my take at the end of that, how is that different than booking on Airbnb, where I get my take from a location, but I have to pay out to the owner?
- [49:07]
Matt Fitzpatrick
Oh, good question. Well, I think Airbnb has one consistent fee. That's the difference. There's actually a fair amount of variation based on the skill set of the expert. Like, you don't have a consistent rate relative to. To the booking amount. That's the biggest difference. So there's huge variety depending on the project, the expertise type, the expert type of what you book on that.
- [49:25]
Harry Stebbings
Are there any other big misnomers that you think are pronounced in the industry where you consistently. I wish people would change the way they think about it.
- [49:34]
Matt Fitzpatrick
Look, I think the biggest one is just the view that when I first started this job, the main pushback I always got was that synthetic data will take over and you just will not need human feedback two, three years from now. And it's interesting from first principles, that actually doesn't make very much sense if you Think through it. Right. If you think about the diversity of tasks that exist in the world and then how long it would take you to get comfortable with the accuracy, it doesn't make any sense. Right. I'll take legal services because it's a really interesting one. Right. A lot of the legal data in the world exists with big law firms. It doesn't even exist in the public. So if you take the corpus of publicly available information that's been commoditized for years at this point point. Right. And so most of the logic is incredibly contextual to language culture, multimodal context and the information stored in individual companies as an example. And so the only way to actually do the fine tuning process consistently and to get it accurate for any specific context is RLHF. And I actually think in my, in my decade, in my McKinsey days, McKinsey Quan Black days, that was the thing I realized was different about traditional ML models versus Gen AI in machine learning. You can back test, you can get to a really clearly statist, basically validated outcome without any human intervention. I think on the gen AI side you are going to need humans in the loop for decades to come. And I think that is something that most people are starting to realize. I think it's always confusing to people when they hear like, oh, that's how models are trained on the back end. I didn't realize that's how the physical validation works. And so I think that's been an interesting evolution curve as people started to.
- [51:01]
Harry Stebbings
Realize that you're profitable.
- [51:02]
Matt Fitzpatrick
Guys, this year we have started to invest a lot more. So I think one of the big differences, historically Invisible had only raised 7 million of primary capital in its entire nine year journey. We initially announced 100, we've actually right now raised 130 million. And so I'm investing very heavily in technology. So we will not be profitable this year. No.
- [51:18]
Harry Stebbings
Can you just take me to that decision? Because this was going to be my question which is like that's a very clear decision to be profitable and profitability comes often at the extent of growth naturally. Can you just take me to that decision making for you and how you thought about it?
- [51:31]
Matt Fitzpatrick
Yeah, look, I mean to me it was a simple one, which is if you think about the dynamics of return on capital, you can either harvest capital or invest capital. And your decision to invest depends on the growth you see as a result of that investment. And I think we're in the greatest environment for growth that has ever existed. I think Invisible is really uniquely positioned to capitalize on that growth too. And so I think of our five core platforms, I think of the growth vectors across both AI training and enterprise. And there were just way too many different things I thought were interesting to invest in. It was the clear, best use of capital. And I looked at, I'm trying to build this for the next 10 to 20 years. And I think if you want to build enterprise value for 10 to 20 years, now is the time to invest and build. And I hope we never get to the harvest stage, but definitely not now.
- [52:14]
Harry Stebbings
Where are you not investing that you want to be investing?
- [52:18]
Matt Fitzpatrick
I think the simplest answer is actual physical world interactions. So what I mean by that is, I think a lot of the most interesting data that we don't even really have access to yet is things that exist in the physical world that are more complicated to acquire and organize. So I'll give you an example. We're serving one of the largest agricultural conglomerates in the US on herd safety. So actually like monitoring risk factors. When should you send a vet for their herd of cows? Basically, that whole process relies on us actually sending four deployed engineers to farms, dropping starlink terminals into those farms, and building out custom computer vision models in those contexts. And I think there are so many different physical world contexts that become really, really interesting, but it does take costs and capital to build those out. I think oil and gas, oil rigs are an interesting one as an example. And so I think physical world interaction patterns are some of the most interesting growth vectors for this, but they do take time and money to invest in robotics being another big part of that.
- [53:14]
Harry Stebbings
One area of investment that I think is interesting is brand. How do you think about Invisible's brand today?
- [53:19]
Matt Fitzpatrick
Well, it's interesting, when I took over, if you looked at the entire public Internet, I think there was one article available. And so we've definitely spent a lot more time this year thinking about is.
- [53:29]
Harry Stebbings
That a deliberate decision?
- [53:31]
Matt Fitzpatrick
I think so, to some degree. I think Invisible has a culture of we believe in doing great work for customers, and we were kind of not really focused on telling the whole world about that.
- [53:39]
Harry Stebbings
Does that become detrimental to the business at some point, though?
- [53:42]
Matt Fitzpatrick
Yeah, look, I do think branding matters a lot. My view now is that it's been very helpful for us to spend time where I spend a lot, so 70% of my time on the road, and I go to a lot of conferences, things like that. And I think building a brand is really important for trust, for awareness, for engagement. And I think also how you tell that story is really important. So I'm very much a believer. One of my favorite quotes, like Mark Andreessen has this idea that when private and public narratives diverge, that is the risk or the opportunity. So, meaning if you say things you don't believe to be true or if everyone's saying things that don't believe true, then what is the actual private narrative? So I think it's been very important to me, to me.
- [54:19]
Harry Stebbings
So can you just help me understand that?
- [54:20]
Matt Fitzpatrick
Yeah. So hypothetically, if I was going around saying we have an out of the box agent that does everything and then that wasn't actually true, that's what either creates opportunity for others or risk for us. That's how I think about it. And so I think what's been very important for me and how our industry.
- [54:34]
Harry Stebbings
I'm sorry, I don't mean to pick a fight with Mark Andreessen, but like, hello Mark. Like our job is to sell and then deliver. Deliver later. Like I'm looking at thinking, well, I'm.
- [54:47]
Matt Fitzpatrick
Well, you know, I guess it's all a question of degrees. And I think in my mind, like I want to say things where the narratives are the same to the public and to what our team thinks, what our customers experience. And so I think that's part of why I have focused on saying some of the nuances of what's not working and not claiming everything works out of the box. And I think that is, that is a different approach, but it's been a core to how we've thought about building the brand is we are building this around Traffic Trust where like I want a company we work with to know that if I say this will work, it will work. And I, I think you only get one chance to do that. Right?
- [55:15]
Harry Stebbings
You agree with fake it till you make it.
- [55:17]
Matt Fitzpatrick
Oh, that's such an interesting question. I think it depends on what faking it means. Right. And, and, and what I. One of the things I think is really complicated about Gen AI is non deterministic. Right. So like if you've never built a machine learning model to do pricing in industrial manufacturing, you can still understand what data is available, understand how the price is being set today, and get pretty comfortable. Comfortable that what you build, if you say you will build it will work. And I think that is okay. I think the challenge of non deterministic systems is there is more risk to faking until you make it. Meaning if you, you can kind of go out and say your agent will do anything, then you actually have to deliver an agent that works. Right. I think that's part of the interesting you're asking about accounting dynamics. I think it's part of the interesting dynamic of like a lot of the contracts that the people will sign right now are like I'll sign for 50 agents to be delivered. But then the question is do you deliver the agent agents? Do they work? And so I think that is a different thing than SaaS. To go back to your earlier question, if I deliver a SaaS box I know it will work. If I deliver an agent in the current world. There was actually a report AWS came out with today. It's interesting, like 70% of agents are actually not even AI agents as you think about. Most of the agent agentic processes today are actually traditional script writing and just traditional automation.
- [56:29]
Harry Stebbings
Right.
- [56:30]
Matt Fitzpatrick
And I think that's why I don't self identify as an agent company actually at all. I think we do AI agents, we do AI workflows are a core part of we do. But we do data, we do training and fine tuning and agents are one tool in the toolkit because I think too much emphasis on them, a lot of the time won't work.
- [56:46]
Harry Stebbings
Did you see the video of the robot going around the house recently and it was like the worst thing ever. It was like 11 minutes to take out a glass and then at the end it was like.
- [56:54]
And this was controlled by Simon in the background and you're like the shittest.
- [56:58]
Robot ever was then controlled by some.
- [57:00]
Weird dude in your back bedroom.
- [57:03]
Like this is so shit.
- [57:05]
Matt Fitzpatrick
I do think that is. Yeah, I did see that. And look, I think robotics another one that will take longer but will be really interesting when it works. But by the way, I think even in that case you'll need more task specific robotics, not just broad based.
- [57:16]
Harry Stebbings
Have you ever faked it till you make it and been caught out and did you learn anything from it?
- [57:21]
Matt Fitzpatrick
So when I first started working and it wasn't even called AI back then, it was kind of of data analytics was what it's actually called. This is probably 12 years ago or 13 years ago now. 12. Probably 12 years ago. And you know, I think the firm gave me a pretty interesting purview to try and explore where I could build out AI offerings across different sectors and customer bases. And I don't think I knew what I was going to build. Candidly, I think that the interesting dynamic is I had a lot of conviction that and partly because some of the things I'd done before that AI could be really useful on a whole host of things from inventory forecasting to pricing to credit underwriting if you just thought intuitively of like the sources of data, the fact that so 70% of the software in America is over 20 years old, most of that data is massively fragmented, not clean. And so a lot of the decisioning that happens, the enterprise is done in a really fragmented way. And this is what I did know. I did know that like you took your average sales rep making a call, most of the time they're like googling some stuff to try and figure out what information. Not now, but this was 12 years ago. They had very little information on the script to say customer information, what they might sell. So I had a lot of conviction that that would work. I did not know what would be most interesting. In fact, there were areas I thought would be really interesting, like banking, that were actually much harder to do this inconsistently, it was somewhat. You mentioned earlier like bank. So. So the average bank spends 93% of its cost of its tech cost on maintain initiatives. 7% go into building new things.
- [58:46]
Harry Stebbings
This is my favorite thing with people that I just had one of the CEOs of a big vibe coding platform on and he was like, if SaaS is Deborah, we're going to build our own.
- [58:54]
Matt Fitzpatrick
I heard that.
- [58:55]
Harry Stebbings
Yeah. I'm just like maintaining, provisioning, updating. Are you high?
- [59:00]
Matt Fitzpatrick
Yeah. If you've never gone through infosec and approvals of the bank, like the banks are banks and look for very good reason banks are much more complicated to do a build like that in. Right. And so I think what was this.
- [59:11]
Harry Stebbings
Event that I was at last week was a bank. They have six and a half thousand people in KYC alone. Six and a half thousand people.
- [59:17]
Matt Fitzpatrick
It's a great example. And so I think when I was doing that in the early days, partly because there was very little media coverage or interest in it, I was kind of figuring stuff out from first principles. And so I think the degree to which I faked it when I make it was I had to figure out other people I worked with and customers that trusted me enough to allow me to co iterate and develop stuff with them. I had to figure out a way to recruit really good people that was actually like. I actually think if you take any business very simplistically, it's a question of can you build trust with customers and co iterate to develop and make things work and then can you recruit unbelievable people to deliver that? And it actually really comes down to recruiting in a lot of ways. I think that that's actually the number one thing we focus on. I think of Us as a talent company as much as anything else. You could argue that. Not to use a sports analogy, but Nick Saban did not build Alabama football with the process. He built it with recruiting the best football players in the country. And I think about that the same way you have to recruit great people. So in some degree, in the early days of that 10, 12 years ago, I was setting a vision and trying to figure stuff out and actually iterating a lot of stuff. And I do think we ended up building a lot of things that really worked. But it took time and it took iteration as much as anything else. It took iteration and trust. So I would say the counterintuitive thing is I didn't fake it. And then I never told people would definitely work. I would actually my entire approach would be to say I think this would work. This is my reasoning why I think it would work. And let's build this and that. Actually a lot of people were very comfortable with that. I think if you go in and say I have an out of the box AI that solves all your problems, people are pretty skeptical.
- [60:43]
Harry Stebbings
I do just want to stay on recruiting because again, I think the show is successful because you put on the hat and you're like, as a startup CEO, one of your biggest jobs is to recruit great people. Having recruited people across different companies now, both McKinsey and now obviously invisible, what would you advise startup CEOs in the earlier stages, knowing all you know now and what it takes to be great at recruiting, acquiring and retaining great talent?
- [61:07]
Matt Fitzpatrick
Yeah, it's probably the topic I spent an enormous amount of time focused on that. It's probably the topic I think about the most because I actually do think if you get amazing people, everything else will follow from that.
- [61:16]
Harry Stebbings
So you agree with the moniker of like hire great people and let them do their work because people have kind of pushed back on that now.
- [61:21]
Matt Fitzpatrick
Yeah, I think not just hire, hire, retain and evolve great people because I actually think you have to give them a platform that they enjoy day to day. I think the two things that I believe that are somewhat counterintuitive is when you recruit a great person and I don't think about role most of the time, meaning I think people are very role focused of like I will hire this person and they will only do oil and gas as an example. Right. But the reality is like really good people will run five to six different roles across your. They'll run seven to eight different products. Particularly on the business side, you may have somebody that does everything from delivery to sales to account lead. And you can be comfortable with that if you hire great all around athletes in a lot of ways. And I think the second thing is it has to be fun. My view on one of the narratives that has gotten a bit lost in the last couple years is if you have a culture that is brutal to work at, people will leave. They might stay around as long as your stock's high, but they're not going to stay. And I think you have to create an environment where people really enjoy going to work every day, where they're intellectually challenged and where they feel like they can unleash creativity. And so I think that's. I spend a ton of time thinking about that.
- [62:25]
Harry Stebbings
Can you just. I don't want to argue back, but I want to build, build great companies myself. I'm trying to with 20 VC and I try to build good cultures. Revolut is a brutal culture to work at, famous for it. But Nick has famously always told me culture's fucking bullshit. Winning is what matters. When people win, they learn more, they earn more, and they grow.
- [62:46]
Matt Fitzpatrick
Yeah.
- [62:47]
Harry Stebbings
And that really is culture. Brutality in bounds drives humans. Is that wrong?
- [62:53]
Matt Fitzpatrick
No, I think it's actually right. And let me caveat, what I said is I think it's also the nature of the business idea. Am I'm in being AI, meaning I actually think that's a very true statement. If what you're trying to do is scale a relatively consistent business model to do one or two things, then that is a function of execution and hiring people to go in very specific roles and do very specific things. Well, and I actually. Sorry, let me caveat my prior comments on that. I think the difference is a lot of what we do is research and exploration. Fundamentally. Right. And so in the AI world it is a different dynamic in that you're trying to figure out very specific problems with customers to solve and build really unique tech. And so I think in that world you do have a different cultural dynamic. It is a research culture as much as it is an implementation culture.
- [63:37]
Harry Stebbings
Is that difficult then when, you know, I just. We do a show every Thursday which is blown up, which is incredibly nice for us as a business. But essentially we have Jason lamkin and Rory O', Driscoll, two VCs and we talk about news and we talked about Sam Altman and war mode. Can you do a war mode then? In the culture of research and AI, where it's maybe more thoughtful, does that work?
- [63:58]
Matt Fitzpatrick
Yeah, there are definitely parts of our like, I think if you take our delivery and operations Teams, they're in war mode quite a bit of the time. So I think again I'm more describing general, I think countercultural beliefs I have on how to hire certain sets of gray people. I don't think it applies to every single function of the company. I would agree with that. I think there are definitely. You have to be able to push really hard to deliver certain outputs and I think we do a great job of that, that. But I also think there have been ideas of every great engineer should be able to spend 30% of their time on new projects as well as sprinting on the existing ones. I think it's paradigms like that that are important.
- [64:29]
Harry Stebbings
What decision are you scared to make but you think about it often?
- [64:34]
Matt Fitzpatrick
Yeah, I think the simplest answer I'd have to that is that growth in this industry relates to the amount of capital you raise. And your earlier question on investment. I do think there's a world in which if you pursue hyperscale growth, it is possible, but you have to invest a lot more to do that. Like every new company, every new customer you onboard you have, it does cost money to do the forward deployed engineering work. You invest more in your tech. And so there is an interesting like do you run a business for consistent steady growth for 20 years or do you try and build something that gets to 50 to $100 billion and becomes game changing? And we have very much tried to operate in a way where I think we have a path to profitability, everything else. But we are going to invest in the near term because I think it is, it is a very interesting time to do that.
- [65:17]
Harry Stebbings
I know you don't like to name names, but I can because when macaw raises like $2 billion and you're like fuck, we need to raise more fucking money.
- [65:25]
Matt Fitzpatrick
It's interesting if you look at the players in our space that there have been very different levels of capital raised and people had success more and less. I actually think a lot of our investment is in different areas than many of our peer set in AI training are focused on. A lot of it's in things like the enterprise, it's in core software platforms that are maybe a little bit different than what other focuses on. So. So I think you can raise a lot of money and the question is where you spend it. Again, I actually think most of the capital we need in the next five years is more enterprise focused. I think we've actually built something on the IT side I feel very, very good about.
- [65:54]
Harry Stebbings
We were talking about recruiting before I went off on a tangent There you now have offices despite being a remote company for several years. Does remote not work?
- [66:05]
Matt Fitzpatrick
Yeah. So we were a fully remote company for nine years until I took over. We've now gone largely in person and we do have some folks that work remote. But we now have offices in New York. We took the old Pinterest space in San Francisco, London, Paris, Poland, D.C. and just opening Austin, Texas now. And I think the interesting thing I've experienced is that is I do think remote. You really struggle to build culture in the same way. So I think that the things I've experienced since we were remote is just a way stronger positive culture of co location which I think people enjoy their work and get to know their coworkers a lot better as a result of it. I think it gives us a lot more depth with customers to be co located in cities where we spend time with them. I mean like if I take London and Paris, we need to be co located with the customers there. It can't just be, you know, someone in a zoom screen.
- [66:51]
Harry Stebbings
In New York, do you see productivity increase exponentially?
- [66:54]
Matt Fitzpatrick
Yes. I think if you take engineering as an example, like I think you can execute engineering tasks remotely, but the process of working through really thorny problems. So I've tripled the size of the engineering team. And what I can tell you is interesting thing is vast majority of those people wanted to be in person. Now I'm not saying that's true of all engineers, but it was interesting how many people, particularly the younger tenures were like, I want to be co located, I want to work through things. And so I don't even mandate office attendance, I just have it in those offices. And we have huge appetite. Like I was with our, we have 40 people in our London office. I was with many of them last night. They were all commenting on how many of them come in voluntarily even on like a Friday where they might not need to because they like being around their peers. I think that I would actually bifurcate two separate things and I don't think they're related. One is the hours you work seven days a week, very flexibly depending on client needs exist from physical co location. I actually don't think they're related. Meaning I think the benefit of integration is if stuff comes up on a Saturday or you're pushing on a new product build like you will work on that Saturday. But if you do that from your, your home, that's totally fine. I think office culture to me is like if you took a hypothetical thought experiment and you said over a Year I think there is a diminishing return from being in the office all the time where you lose flexibility. So as an example, if I said we were physically, we were remote 100% of the time, that would not work at all. If I said we were physically in the office six days a week, I think that is overkill and you lose great people, particularly senior enterprise folks don't want to be in the office on Saturdays. But what I think we found a nice balance between is people come to the office most days, people really enjoy being with their colleagues, they work most days, but they can do it from their own home on the weekends. And I think that sort of flexibility is good.
- [68:36]
Harry Stebbings
Final one, before you do a quick fire, what did you believe about management that you now no longer believe?
- [68:42]
Matt Fitzpatrick
I think two things I would highlight. One is that I think control is a bit of a fallacy depending on the volume of things you have going on. Meaning I actually think the question earlier on hiring great people, if you're serving several, you know, let's say a couple of years from now, you're serving a couple hundred customers on different topics, you actually need to have values, consistent tooling, consistent approaches. But you need to empower all those teams at the edge to operate and do what they will. And so actually one of the big focuses I've made over the course of the year is to reduce a lot of our hierarchy, make the organization way more flat so that companies that people at the edge serving customers are empowered to make decisions. They have decision making frameworks, they have consistent tooling, but they are empowered. I think trying to control that centrally maybe works in like a manufacturing business, but you, you lose a lot of latency of decision making. So you know, I think if you look at like there's a lot of, interestingly military history that would say the same thing. That is like actually if you look at the function of an army, at some point it moves into people in the field make the decisions. And so you have to have the training, the strategy, recruiting to do that. And then you have to empower your teams to work. And I think I, I think about a lot of that very similarly. I think the second thing I think a lot about is in the AI world at least, strategy is a somewhat overrated concept. And what I mean by that is I think actually all strategy. I was talking to a CEO in the biotech space and he was saying that strategy is very important for them because every time they make a capital decision, it's a seven year capital cycle, right? And so in that case, strategy makes a lot of sense. But in the AI world, one thing that's been interesting to me is every three months the entire world changes. And I've just had to get very comfortable with that dynamic. You have to think about your investment life cycle as core beliefs you have and then 30 to 40% of things that you iterate constantly based on new tech. So there is tech that you're going to build, like a new voice agent comes out that will become obsolete and you have to just be very comfortable that you're building an interoperable set of frameworks that you can integrate the new tech into. And that has to be a core function of the business is five year strategic plan. Planning is not a useful exercise right now in a lot of ways. I think you want to think about five years in terms of the cultural context you build the organizational like the institutional memory to use the seven Powers framework. But the actual iteration cycles are much, much faster. And I think if you don't react quickly that that does not sustain. And now I think enterprise, the interesting flip side of that is enterprise sales cycles, for example, are much longer. So it's not like you can't survive unless you're making decisions. But, but I do think the big thing is a lot of the tech being developed changes every two to three months. And so you need to be constantly incorporating that into what you build.
- [71:14]
Harry Stebbings
Final, final one, I promise for a quick fire. You said about always being traveling and you mentioned a girlfriend earlier. How do you make that work? And what would you advise me as like, hey, tips and tricks to not have a severely pissed off girlfriend most of the time?
- [71:28]
Matt Fitzpatrick
I think the first thing is to find a great girl who understands that you are really passionate about what you're doing and is, is supportive of that. I think my girlfriend Claudia has been, has been great on that front. I am very appreciative of that. But look, I mean, it's tough. I'm on the road probably 60% of the time. If you look at my last four or five weeks. Riyadh, Geneva, Paris, Berlin, London, San Francisco, Boston, Singapore, now London again. So I mean that's a.
- [71:53]
Harry Stebbings
Do you enjoy this?
- [71:54]
Matt Fitzpatrick
I do in some ways. I think that I feel very lucky to be building something at this particular time and with a group of people I love working with. This happens to be what I spent my last decade, decade doing. And it happens to suddenly now be with like what a lot of people want to do, which is great. And so I feel very lucky because of that. And so every day I wake up and see what else can I do to be to kind of push that forward. And so I do kind of live on the road. But look, I think you, I think some of the things I've tried is like, you know, you figure out things like FaceTime, you make sure you keep the cadence interaction high because being on the road is tough. But I also don't think it's forever. I also think I'm in that fun stage of trying to take something to like we kind of went zero to one and now we're trying to go one to end but we're not yet get a fully mature public company or anything like that. And so I think she's been very understanding throughout that process.
- [72:38]
Harry Stebbings
Are you ready for a quick fire round?
- [72:40]
Matt Fitzpatrick
Yeah.
- [72:40]
Harry Stebbings
Okay. OpenAI at 500 billion or anthropic at 360 billion? Which would you rather invest in?
- [72:47]
Matt Fitzpatrick
I do not comment on any players in the model builder space for a variety of reasons.
- [72:54]
Harry Stebbings
You can see why it's the discomfort round. What's the most underrated in fear company today?
- [72:59]
Matt Fitzpatrick
Databricks. Which is you're going to be like, well, they're very rated but look, I think their tech is great and I think that it's interesting in a lot of ways. The most useful foundation for AI is really good databricks infrastructure. I think when I hear a customer has them, I'm always very happy.
- [73:13]
Harry Stebbings
What's the best advice that you've been given that you most frequently go back to?
- [73:18]
Matt Fitzpatrick
We kind of talked about this a little bit earlier, but a CEO that I respect a lot. When I took the role, I asked him his advice that like, what's the best way to think about a team? And he said, look, your job as a CEO is to do three things really well, recruit great people, create a culture where they love working together and build great things and try and make them all extremely rich. And I think it's a funny framework, but I think an interesting way to think about that is my responsibility to employees. I want to find great people, help them enjoy each other and then build something that becomes big and helps all of them achieve their dreams.
- [73:50]
Harry Stebbings
What's one widely held belief about AI.
- [73:52]
That you think is completely wrong, that.
- [73:54]
Matt Fitzpatrick
Out of the box agents will solve everything with a push of a button. That is, I think the biggest misconception now is that I think many people were hoping the adoption curve will be I buy something, I just push it in my business and it takes a whole process and fixes it. And I Think they're realizing it requires training, fine tuning, and a whole host of process redesign and business ownership.
- [74:12]
Harry Stebbings
You are me today. You have a new $400 million fund, and you're a partner in the fund with me. Where should we be investing? Where most people are not. Because everyone is investing in agents out of the box.
- [74:22]
Matt Fitzpatrick
Well, yeah, look, you know, I think it's an interesting question because I think a lot of the reason people are investing in the agents out of the box is that they're trying to apply a SaaS paradigm of what's worked historically to AI, which is challenging. The model building layer is clearly producing amazing returns. I think the AI agent layer is more complicated now. Where I think that's also complicated is the application layer is tricky too. And I think you hear a lot of commentary on this. Like, like many of the applications may or may not work. They're not really getting full workflow embedding. They're more of like, kind of nice to have in workflow context. So actually, my counterintuitive take would be one interesting question of the paradigm now is whether new companies built around AI get distribution faster than big companies figure out how to adopt AI. I think that's like the interesting paradigm for our society. And so I think some of the most interesting new businesses are actual businesses using AI in the physical world that are AI native and that will be highly disruptive. So you mentioned revolut in banking, for example, or you could go into like loan servicing. There's many different areas where people are standing up new businesses. One of the most interesting stats I've heard recently is if you look at Y Combinator's recent class, I think it's like the largest. It's 2x the revenue of any prior class. And many of those are businesses that are actually serving a customer need, not selling that customer software, if that makes sense. And so I think from an adoption standpoint, one way to do this is to bet on AI agents, which are more of like a SaaS paradigm, who will sell stuff to customers. And the other way to think about it is what are business models that will change because of this? I think there's a whole list of like, you know, gen native services businesses are very like, you know, tax accountancies, et cetera are really interesting examples of that.
- [76:02]
Harry Stebbings
Again, you're a partner with me in the fund.
- [76:04]
Matt Fitzpatrick
Yeah.
- [76:04]
Harry Stebbings
Do we just get used to a world of lower margins? Is that how this business plays out? Is the world of 70, 80% software margins over?
- [76:12]
Matt Fitzpatrick
First of all, challenge that 70, 80% software margins actually ever existed. What I mean by that is there's the gross margin. But if you look at profitability in public software multiples, it's fascinating, right? In the last two years you've seen public software multiples go from 20x to 10x partly because of growth changes and partly because as they move profitable, their growth slows materially. And what you realize is like I actually would take the flip side of this, which is the integrated units will be very, very profitable because the way they grow, they'll be able to acquire customers faster, build them things that are good faster, and so they won't have the box stickiness, but they'll also, I would argue a lot of those software companies below the line were not that profitable.
- [76:50]
Harry Stebbings
When you look forward to the next 10 years, final one, what are you most excited about? Like, you know, for me, my mother's got Ms. I look at potential, potential advancements in our mass drug discovery treatment pathways. What are you most excited for? I like to end on a tone of optimism.
- [77:04]
Matt Fitzpatrick
Yeah. I think despite some of my, what I call realism on enterprise adoption, I actually am an AI optimist and I actually think that the current narrative on some of the risks are far outweighed by some of the benefits. And just to give a couple examples, and I'll go through four, including healthcare. If you take energy as an example, there's a lot of question around data center implications for, for, for energy. But you do the math. Right now, data centers are about 1% of total global electricity usage. AI data centers are 0.25 to 0.5% of that. So actually really small. I don't know. Cooling, electricity, air conditioners is 14 to 20% of global electricity usage. AI has so many different ways of grid optimization, cooling, where I mean, the World Economic Forum just came out and said this. It's going to be massively net, net positive from a environmental environment impact standpoint. So I think energy is one where, if you think about all the energy needs we're going to have and the investment now going into clean energy because of all this, I think we'll actually be in a much better place 10 years from now. I think healthcare is another interesting one. If you look at US healthcare, we spend 14,000 per capita per year on patients in the US so that's like a rough spend, that's 2 1/2 to 3x like Germany and Canada spend as an example. If you then break down the context of that, you know, 9% of that roughly is, is administrative, something like 25% of it's one waste. And then actual cost of care is really challenging. I mean Johns Hopkins just released a stat that 250,000 deaths a year happen because of avoidable errors. You see things in AI like 20% better identification of breast cancer risk, for example. So I think actually healthcare is another one where the cost framework for healthcare has been not good over the last 20 years and the cost of care improvements will be really material if AI works well. So I think that's another one. I think the one I'm probably most excited about is education. If you're a kid growing up in any socioeconomically disadvantaged city in the world, your ability to learn about any topic on earth incredibly quickly is better now than it will has ever been at any point in history. You can take any topic on earth and with just an Internet connection learn, you can go through and you can pick your topic. And I think one of the reasons that's particularly important is educational system we've had for the last 10, 15 years, actually 50 years doesn't really work. I mean we have have massive K through 12 challenges with STEM topics in the U.S. for example, we have huge learning gaps largely driven by sociodemographic context. And most of our educational system is based around like teach people biology, English and history and like not teach them about basic things like FICO scores or how to do coding. And so and to add to all that, the college system has, has created a student debt crisis where people, way too many people are to going, going to colleges that are not worth going to for and taking on enormous amounts of debt to do it. So I actually think again I think the way our educational system will shift, will function, will shift material. We're a talent assessment company. An enormous amount of people we bring in did not go to college and we assess them on cognitive aptitude and skill. And so I think the really positive note I would leave on is I think the way that people learn the topics they learn, the way we look at resumes and how to screen and assess people, people will move in a really positive direction and I think a very different one than we've had the last 100 years.
- [80:08]
Harry Stebbings
Absolutely thrilled to hear that there is value in non college or dropouts. As a dropout myself, this has been so much fun to do. Matt, thank you so much for being so flexible with the topic type. You've been fantastic dude.
- [80:19]
Matt Fitzpatrick
Thank you for having me.
- [80:22]
Harry Stebbings
But before we leave you today, are you drowning in AI tools? ChatGPT for writing notion for.com Gmail for email, Slack for comms, and you're constantly copy pasting between them all, losing context and losing time. This is the AI productivity tax and it's killing your output. At 20 VC, we're all about speed of execution and Superhuman is the AI productivity suite that gives you superpowers everywhere you work. With the intelligence of Grammarly, mail and coda built in, you can get things done faster and collaborate seamlessly. Finally, AI that works where you work, however you work. Superhuman gets you from day one with zero learning curve and it's personalized to sound like you at your best, not like everyone else using generic AI. Get AI that works where you work. Unlock your Superhuman potential. Learn more@superhuman.com podcast that's superhuman humanhuman.com podcast and speaking of tools that give you an edge, that's exactly what AlphaSense does for decision making. As an investor, I'm always on the lookout for tools that really transform how I work. Tools that don't just save time, but fundamentally change how I uncover insights. That's exactly what AlphaSense does. With the acquisition of Tagus, AlphaSense is now the ultimate research platform built for professionals who need insights they can trust.
- [81:45]
Finding fast.
- [81:46]
I've used Teagus before for company deep dives right here on the podcast. It's been an incredible resource for expert insights, but now with AlphaSense leading the way, it combines those insights with premium content, top broker research and cutting edge generative AI. The result? A platform that works like a supercharged junior analyst delivering trusted insights and analysis on demand. Alphasens has completely reimagined fundamental research, helping helping you uncover opportunities from perspectives you didn't even know how they existed. It's faster, it's smarter, and it's built to give you the edge in every decision you make to any VC listeners. Don't miss your chance to try AlphaSense for free. Visit AlphaSense.com 20 to unlock your trial. That's AlphaSense.com 20 and if AlphaSense helps you make smarter decisions, Daily Body Coach helps you build smarter habits. You know how so many founders and execs say they'll finally take care of.
- [82:41]
Their health once things slow down?
- [82:43]
Well, they never do. Running a business is a marathon made of high intensity sprints and taking care of yourself is what gets you through.
- [82:51]
Those times, performing at your best both professionally and personally.
- [82:55]
This is exactly where Daily Body Coach comes in. Daily Body Coach is a complete high.
- [83:00]
Touch service for busy founders and executives combined.
- [83:03]
Combining personalized nutrition and training with psychology based coaching to help you not just follow a plan, but actually build the systems, habits and mindset to stay at the top of your game. Built by an exited founder and led by certified experts with Masters and PhD.
- [83:20]
Level credentials, daily Body Coach is fully.
- [83:22]
Tailored to your life. Whether you're traveling, dining out or in back to back meetings. You get daily accountability, data driven insights from daily DEXA scans and blood work.
- [83:32]
And a highly certified team backing you.
- [83:34]
If you're serious about performing at your best physically and mentally, go to dailybodycoach.com 20vc. That's dailybodycoach.com 20vc and take the next step.